Correlations between APOBEC_MutLoad_MinEstimate and mRNAseq expression

Overview

Introduction

This pipeline attempts to calculate the pearson correlation between APOBEC_MutLoad_MinEstimate and mRnaseq data of each gene across samples to determine if the APOBEC_MutLoad_MinEstimate also result in differential expressions.

Summary

The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are -0.0969, -0.0672, -0.0458, -0.0256, -0.0072, 0.0123, 0.0337, 0.0583, 0.0912, respectively.

Results

Correlation results

Number of samples used for the calculation are shown in Table 1. Figure 1 shows the distribution of calculated correlation coefficients and quantile-quantile plot of the calculated correlation coefficients against a normal distribution. Table 2 shows the top 20 genes ordered by the value of correlation coefficients.

Table 1. Counts of mRNAseq and number of samples in APOBEC_MutLoad_MinEstimate and expression data sets and common to both

Category	APOBEC_MutLoad_MinEstimate	Expression	Common
Sample	496	501	490

Figure 1. Summary figures. Left: histogram showing the distribution of the calculated correlations across samples for all Genes. Right: QQ plot of the calculated correlations across samples. The QQ plot is used to plot the quantiles of the calculated correlation coefficients against that derived from a normal distribution. Points deviating from the blue line indicate deviation from normality.

Table 2. Get Full Table Top 20 genes ranked by correlation coefficients

geneID	cor	p-value	q-value
C19orf30\|284424	0.2996	0.000160091990133449	0.0150002859505247
CCL1\|6346	0.2995	5.65642783620923e-05	0.0087343957313152
C4orf17\|84103	0.2723	0.00038586307187094	0.0214090315973816
IL1F7\|27178	0.265	0.000149455708633228	0.0146125445560423
CYP2C9\|1559	0.2496	0.00122584038559381	0.0379100671899525
PSG6\|5675	0.2485	0.000584068984720965	0.0262029951000752
CEACAM8\|1088	0.2479	0.000250374843245638	0.017748007449079
RETN\|56729	0.2422	3.91832670065817e-06	0.00237148529631148
IL1F9\|56300	0.2382	0.00200035313776015	0.047411060142106
CAPN2\|824	0.2363	1.20891728627726e-07	0.000543710549503196
AZU1\|566	0.2307	4.37725124169397e-06	0.00246083593243983
IFNB1\|3456	0.23	0.00179067891770845	0.0440293613802182
CKLF\|51192	0.2283	3.26873343059475e-07	0.000980075240273326
GABRA1\|2554	0.2267	3.13556092081946e-05	0.00714034695766355
FAM163B\|642968	0.225	0.00387739465356907	0.0650962987670342
CCL24\|6369	0.2196	0.00156083418062947	0.0414762288176132
SLC20A1\|6574	0.2178	1.13186721684144e-06	0.00196856583113628
CACYBP\|27101	0.2155	1.46597339689691e-06	0.00196856583113628
FCRLB\|127943	0.2149	1.58577323894349e-06	0.00196856583113628
ADRA1A\|148	0.2148	2.88223659161702e-05	0.00691352483775868

Methods & Data

Input

Gene level (TCGA Level III) mRNAseq expression data and APOBEC_MutLoad_MinEstimate derived by Mutation_APOBEC pipeline were used to do this analysis. Pearson correlation coefficients were calculated for APOBEC_MutLoad_MinEstimate and each gene across all the samples that were common.

Correlation across sample

Pearson correlation with pairwise.complete.obs was used to do this analysis.